New methods for biological sequence alignment

190 Pages·2011·2.88 MB·French

New methods for biological sequence alignment
Marta L. Gîrdea
To cite this version:
Marta L. Gîrdea. New methods for biological sequence alignment. Bioinformatics [q-bio.QM]. Uni-
versité des Sciences et Technologie de Lille - Lille I, 2010. English. ￿NNT: ￿. ￿tel-00833311￿
HAL Id: tel-00833311
https://theses.hal.science/tel-00833311
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New methods for biological sequence
alignment
`
THESE
pr´esent´ee et soutenue publiquement le 10 d´ecembre 2010
pour l’obtention du
Doctorat de l’Universit´e de Lille 1 – Sciences et Technologies
(sp´ecialit´e informatique)
par
Marta Gˆırdea
Composition du jury
Rapporteurs : E´ric Rivals, DR CNRS LIRMM, Universit´e Montpellier 2
Michal Ziv-Ukelson, Professeur Universit´e Ben Gurion du Negev, Israel
Examinateurs : Emmanuel Barillot, DR INRA Institut Curie, Paris
Michael Brudno, Professeur Universit´e de Toronto, Canada
Clarisse Dhaenens, Professeur Universit´e Lille 1
Advisors : Gregory Kucherov, DR CNRS LIFL-INRIA, Universit´e Lille 1
Laurent Noe´, Maˆıtre de conf´erences LIFL-INRIA, Universit´e Lille 1
UNIVERSITE´ DE LILLE 1 – SCIENCES ET TECHNOLOGIES
E´COLE DOCTORALE SCIENCES POUR L’INGE´NIEUR
Laboratoire d’Informatique Fondamentale de Lille — UMR 8022
U.F.R. d’I.E.E.A. – Bˆat. M3 – 59655 VILLENEUVE D’ASCQ CEDEX
T´el. : [phone] – T´el´ecopie : [phone] – email : [email protected]

Acknowledgements
I would like to thank my advisor Gregory Kucherov, whose expertise was very important in
this research work, who has always managed to be present and stay involved regardless of any
geographic distance, and who has taught me many things, from science to written and verbal
communication.
I address a special thank you to my second advisor Laurent No´e for having closely guided
and advised me during these three years, for his constant support, patience, friendliness, and
active involvement which have played a crucial role in the progress of this thesis.
Variousideasrelatedtotheworkpresentedhere,eitheralreadyincludedorplannedasfuture
developments, have emerged from interesting discussions with Emmanuel Barillot, Valentina
Boeva, Michael Brudno, Martin Figeac, Mathieu Giraud, Maude Pupin, E´ric Rivals, H´el`ene
Touzet,Jean-St´ephaneVarr´e,aswellasfromquestionsandsuggestionsraisedbytheanonymous
reviewers from conferences and journals where we submitted article manuscripts.
I would then like to thank all the present and former members of SEQUOIA with whom
I had the pleasure to interact during my three years in this team, for having created a work
environment that was not only scientifically rich, but also particularly warm and friendly.
Last but not least, I would like to thank my former professor Liviu Ciortuz for having
opened my interest towards this research field, and all my former professors at the ”Al. I. Cuza”
University of Ia¸si for their contributions to my education.
i
Acknowledgements
ii
Abstract
Biologicalsequencealignmentisafundamentaltechniqueinbioinformatics, andconsistsofiden-
tifying series of similar (conserved) characters that appear in the same order in both sequences,
and eventually deducing a set of modifications (substitutions, insertions and deletions) involved
in the transformation of one sequence into the other. This technique allows one to infer, based
on sequence similarity, if two or more biological sequences are potentially homologous, i.e. if
they share a common ancestor, thus enabling the understanding of sequence evolution.
This thesis addresses sequence comparison problems in two different contexts: homology
detection and high throughput DNA sequencing. The goal of this work is to develop sensitive
alignment methods that provide solutions to the following two problems: i) the detection of
hidden protein homologies by protein sequence comparison, when the source of the divergence
are frameshift mutations, and ii) mapping short SOLiD reads (sequences of overlapping di-
nucleotides encoded as colors) to a reference genome. In both cases, the same general idea is
applied: to implicitly compare DNA sequences for detecting changes occurring at this level, while
manipulating, in practice, other representations (protein sequences, sequences of di-nucleotide
codes) that provide additional information and thus help to improve the similarity search. The
aim is to design and implement exact and heuristic alignment methods, along with scoring
schemes, adapted to these scenarios.
R´esum´e
L’alignement de s´equences biologiques est une technique fondamentale en bioinformatique,
et consiste `a identifier des s´eries de caract`eres similaires (conserv´es) qui apparaissent dans le
mˆeme ordre dans les deux s´equences, et `a inf´erer un ensemble de modifications (substitutions,
insertions et suppressions) impliqu´ees dans la transformation d’une s´equence en l’autre. Cette
technique permet de d´eduire, sur la base de la similarit´e de s´equence, si deux ou plusieurs
s´equences biologiques sont potentiellement homologues, donc si elles partagent un ancˆetre com-
mun, permettant ainsi de mieux comprendre l’´evolution des s´equences.
Cette th`ese aborde les probl`emes de comparaison de s´equences dans deux cadres diff´erents:
lad´etectiond’homologiesetles´equenc¸age`ahautd´ebit. L’objectifdecetravailestded´evelopper
des m´ethodes d’alignement qui peuvent apporter des solutions aux deux probl`emes suivants: i)
la d´etection d’homologies cach´ees entre des prot´eines par comparaison de s´equences prot´eiques,
lorsque la source de leur divergence sont les mutations qui changent le cadre de lecture, et ii) le
mapping de reads SOLiD (s´equences de di-nucl´eotides chevauchantes cod´es par des couleurs) sur
un g´enome de r´ef´erence. Dans les deux cas, la mˆeme id´ee g´en´erale est appliqu´ee: comparer im-
plicitement les s´equences d’ADN pour la d´etection de changements qui se produisent a` ce niveau,
en manipulant, en pratique, d’autres repr´esentations (s´equences de prot´eines, s´equences de codes
di-nucl´eotides) qui fournissent des informations suppl´ementaires et qui aident a` am´eliorer la
recherche de similarit´es. Le but est de concevoir et d’appliquer des m´ethodes exactes et heuris-
tiques d’alignement, ainsi que des systemes de scores, adapt´es `a ces sc´enarios.
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Abstract
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Table of contents
List of Figures ix
List of Tables xiii
De nouvelles m´ethodes pour l’alignement des s´equences biologiques 1
Introduction 13
I State of the art 17
1 Biological context 19
1.1 Nucleic acids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
1.1.1 The deoxyribonucleic acid (DNA) . . . . . . . . . . . . . . . . . . . . 19
1.1.2 The ribonucleic acid (RNA) . . . . . . . . . . . . . . . . . . . . . . . 21
1.2 Proteins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
1.3 Central dogma of molecular biology . . . . . . . . . . . . . . . . . . . . . . . 24
1.3.1 Transcription . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
1.3.2 Translation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
1.3.3 DNA replication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
1.4 Mutations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
2 DNA Sequencing 31
2.1 First approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
2.1.1 Chemical sequencing: the Maxam method . . . . . . . . . . . . . . . 31
2.1.2 The chain-terminator method: Sanger . . . . . . . . . . . . . . . . . . 31
2.2 Next generation sequencing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
2.2.1 Pyrosequencing: Roche/454 . . . . . . . . . . . . . . . . . . . . . . . 33
2.2.2 Cyclic reversible termination: Illumina/Solexa Genome analyzer . . . 34
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Table of contents
2.2.3 Sequencing by ligation: SOLiD . . . . . . . . . . . . . . . . . . . . . . 34
2.3 Next-next generation sequencing . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.3.1 Semiconductor sequencing: Ion Torrent . . . . . . . . . . . . . . . . . 36
2.3.2 Nanopore sequencing . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.4 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3 Algorithms and tools for biological sequence alignment 39
3.1 Biological sequence representation . . . . . . . . . . . . . . . . . . . . . . . . 39
3.2 Pairwise sequence alignment . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.2.1 Definition and representation . . . . . . . . . . . . . . . . . . . . . . . 40
3.2.2 Score . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.2.3 Pairwise alignment algorithms . . . . . . . . . . . . . . . . . . . . . . 43
3.3 Defining sequence similarity via substitution scores. . . . . . . . . . . . . . . 47
3.3.1 Amino acids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.3.2 DNA substitution scores . . . . . . . . . . . . . . . . . . . . . . . . . 49
3.3.3 Codon substitution models . . . . . . . . . . . . . . . . . . . . . . . . 49
3.3.4 Statistical significance measures for alignment scores. . . . . . . . . . 51
3.4 Seeding techniques for sequence alignment . . . . . . . . . . . . . . . . . . . 52
3.4.1 Look-up tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
3.4.2 First implementations of heuristic approaches: FASTA and BLAST . 53
3.4.3 Spaced seeds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
3.4.4 Designing spaced seeds . . . . . . . . . . . . . . . . . . . . . . . . . . 55
3.4.5 Other types of seeds . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
II Frameshift discovery via protein back-translation 65
4 Context and motivation 67
4.1 Problem statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
4.2 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
4.2.1 Amino acid substitution score matrices for frameshift detection. . . . 68
4.2.2 Dynamic programming alignment algorithms that handle frameshifts 71
4.2.3 Protein back-translation and alignment of sequence graphs . . . . . . 71
4.3 Contributions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
5 Alignment of protein sequences with a frameshift 75
5.1 Back-translation graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
5.1.1 Back-translation graph representation . . . . . . . . . . . . . . . . . . 75
vi
5.1.2 Compact back-translation graph representation. . . . . . . . . . . . . 77
5.1.3 Space complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
5.1.4 Reverse complementary of a back-translation graph . . . . . . . . . . 79
5.2 Alignment algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
5.2.1 The dynamic programming table . . . . . . . . . . . . . . . . . . . . . 80
5.2.2 Recurrence relations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
5.2.3 Handling frameshifts . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
5.2.4 Traceback: retrieving the alignment . . . . . . . . . . . . . . . . . . . 87
5.2.5 Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
5.3 Scoring scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
5.3.1 Method for obtaining translation-dependent scores . . . . . . . . . . . 90
5.3.2 Score evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
5.3.3 Comparison with classic amino acid and DNA substitution scores . . 94
6 Seeds for heuristic similarity search in proteins with a frameshift 97
6.1 The concept of frameshift seeds . . . . . . . . . . . . . . . . . . . . . . . . . 97
6.1.1 Informal introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
6.1.2 Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
6.2 Similarity search procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
6.3 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
6.3.1 Modeling frameshifted alignments . . . . . . . . . . . . . . . . . . . . 104
7 Experiments and discussion 109
7.1 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
7.2 Experimental results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
7.3 Conclusions and perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
Published papers and talks 115
III Algorithms for mapping SOLiD TM reads 117
8 Context and motivation 119
8.1 Properties of the 2-base SOLiD encoding . . . . . . . . . . . . . . . . . . . . 119
8.2 Challenges of mapping SOLiD short reads . . . . . . . . . . . . . . . . . . . . 123
8.3 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
8.3.1 Read mapping software tools . . . . . . . . . . . . . . . . . . . . . . . 125
8.3.2 Seeding techniques for color read mapping . . . . . . . . . . . . . . . 125
8.3.3 Alignment algorithms for color sequences . . . . . . . . . . . . . . . . 126
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